数智化转型为中医药知识的现代化管理与智能服务提供了重要机遇，然而中医药知识体系固有的术语模糊、多源异构及语义复杂等特性，对其结构化组织与智能化应用构成挑战。文章构建了一种“模型生成-人工修正-反馈优化”人机协同中医药语义知识组织与知识服务框架，通过融合大语言模型的自动化抽取能力与领域专家的人工校验机制，实现了对中医药文献中实体与关系的精准识别与动态优化。针对中医胃病防治的典型案例，验证了该框架在辅助临床诊疗、实现个性化药物推荐及支持主动健康管理等方面的应用潜力，助力中医药知识的系统性转化、智能化服务以及与现代医学的深度融合。

The digital and intelligent transformation presents significant opportunities for the modernization and intelligent servicing of Traditional Chinese Medicine (TCM) knowledge. However, the inherent characteristics of the TCM knowledge system—such as ambiguous terminology, multi-source heterogeneity, and semantic complexity—pose considerable challenges to its structured organization and intelligent application. This paper constructs a human-in-the-loop TCM semantic knowledge organization and service framework based on a "model generation—human correction—feedback optimization" paradigm. By integrating the automated extraction capabilities of large language models with the manual verification mechanisms of domain experts, the framework achieves precise identification and dynamic optimization of entities and relationships within TCM literature. Using the prevention and treatment of gastric diseases in TCM as a case study, the framework's application potential is validated in areas such as assisting clinical diagnosis and treatment, enabling personalized medication recommendations, and supporting proactive health management. This work contributes to the systematic transformation of TCM knowledge, its intelligent service delivery, and its deeper integration with modern medicine.